/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    Clusterer.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.clusterers;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Interface for clusterers. Clients will typically extend either
 * AbstractClusterer or AbstractDensityBasedClusterer.
 *
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @version $Revision$
 */
public interface Clusterer {

    /**
     * Generates a clusterer. Has to initialize all fields of the clusterer that are
     * not being set via options.
     *
     * @param data set of instances serving as training data
     * @exception Exception if the clusterer has not been generated successfully
     */
    void buildClusterer(Instances data) throws Exception;

    /**
     * Classifies a given instance. Either this or distributionForInstance() needs
     * to be implemented by subclasses.
     *
     * @param instance the instance to be assigned to a cluster
     * @return the number of the assigned cluster as an integer
     * @exception Exception if instance could not be clustered successfully
     */
    int clusterInstance(Instance instance) throws Exception;

    /**
     * Predicts the cluster memberships for a given instance. Either this or
     * clusterInstance() needs to be implemented by subclasses.
     *
     * @param instance the instance to be assigned a cluster.
     * @return an array containing the estimated membership probabilities of the
     *         test instance in each cluster (this should sum to at most 1)
     * @exception Exception if distribution could not be computed successfully
     */
    public double[] distributionForInstance(Instance instance) throws Exception;

    /**
     * Returns the number of clusters.
     *
     * @return the number of clusters generated for a training dataset.
     * @exception Exception if number of clusters could not be returned successfully
     */
    int numberOfClusters() throws Exception;

    /**
     * Returns the Capabilities of this clusterer. Derived classifiers have to
     * override this method to enable capabilities.
     *
     * @return the capabilities of this object
     * @see Capabilities
     */
    public Capabilities getCapabilities();

}
